Data Journalism and FOSS4G: Tools to face negationism and Pandemic
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FOSS4G Argentina 202165 / 237
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00:00
Self-organizationPresentation of a groupMereologyWater vaporLevel (video gaming)Point (geometry)View (database)Different (Kate Ryan album)BitData managementObject (grammar)CASE <Informatik>Open sourceProjective planeNeighbourhood (graph theory)SoftwareSoftware developerParameter (computer programming)Programming paradigmMultiplication signDisk read-and-write headDistribution (mathematics)CollaborationismSystem callWordMathematical analysisProcess (computing)Barrelled spaceGoodness of fitState of matterMetropolitan area network19 (number)Graph coloringMixed realityAuthorizationMathematical singularityMeeting/InterviewComputer animation
04:57
Hand fanReal numberNegative numberPlotterGraph (mathematics)Multiplication signPanel painting
05:32
Physical systemVector potentialIntegrated development environmentWordRight angleInternet forumDifferent (Kate Ryan album)Physical lawIntegrated development environmentLie groupWebsiteThomas BayesAreaDemosceneLogic gateArmComputer animation
07:33
Integrated development environmentPiMUDDiscrete element methodFreewareKey (cryptography)WordData managementComputer animation
08:42
Finitary relationForestTemporal logicVariable (mathematics)System identificationProof theoryIntegrated development environmentGraph (mathematics)Universe (mathematics)Mathematical analysisTask (computing)ConcentricPoint (geometry)Hill differential equationLength of stayTheory of relativityCASE <Informatik>Physical systemProjective planeCross-correlationCore dumpForestStandard deviationLocal ringIndependence (probability theory)ArmDemosceneUsabilityObject (grammar)Endliche ModelltheorieEstimatorComputer animation
13:00
DiameterPhysical systemBitDiameterGraph (mathematics)Dimensional analysisPoint (geometry)
13:42
Maxima and minimaService (economics)Endliche ModelltheorieComputer-generated imageryProcess (computing)Gamma functionState of matterPersonal digital assistantLevel (video gaming)Parallel portCausalityMedizinische InformatikData miningWave packetMultiplication signComputer virusUniverse (mathematics)Natural numberSystem callValidity (statistics)19 (number)InformationPoint (geometry)PredictabilitySelf-organizationCASE <Informatik>State of matterPhysical systemLevel (video gaming)Endliche ModelltheorieWorkstation <Musikinstrument>Cubic graphGroup actionRule of inferenceLimit (category theory)outputHypermediaLine (geometry)ForestProcess (computing)Uniqueness quantificationWordMathematical analysisSet (mathematics)1 (number)TwitterService (economics)Flow separationConcentric
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SineLevel (video gaming)CASE <Informatik>State of matterInformationFlow separation19 (number)Computer animation
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State of matterLevel (video gaming)Cartesian closed categoryAriana TVMultimediaCASE <Informatik>Disk read-and-write headWordInstance (computer science)Level (video gaming)State of matterCubic graphComputer animation
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MultimediaAriana TVEndliche ModelltheorieMereologyTraffic reportingCASE <Informatik>Point (geometry)SequelPhysical systemProcess (computing)Goodness of fitGraph (mathematics)Message passingLabour Party (Malta)
25:27
ForestCoefficient of determinationVideo gameProcess (computing)Forcing (mathematics)Design by contractTrailMeeting/Interview
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Goodness of fitWorkstation <Musikinstrument>Projective planeInformationSource codeComputer animation
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Link (knot theory)Group actionTraffic reportingSelf-organizationLink (knot theory)MereologyDependent and independent variablesRight angleProjective planeControl flowPosition operatorInformationMultiplication signSystem callShared memorySoftwareAuthorizationPhysical lawReading (process)Presentation of a groupWhiteboardOffice suiteGraph (mathematics)ArmComputer animation
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Computer-generated imageryPresentation of a groupResultantSound effectEndliche ModelltheorieRight angleControl flowMathematical analysisEvent horizonSystem callInverse elementGoodness of fitAxiom of choiceData storage deviceMeeting/Interview
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Analytic continuationLattice (order)Meeting/Interview
Transcript: English(auto-generated)
00:05
Thank you. Hello everybody. I'm really happy to be here. I'm part of the organization committee, so this is really nice to be here as a presenter. Thanks, Ken, for being session leaders and all to be here.
00:25
Ignó, Andrea and Jan. And I'm happy to be in this session together with them because I'm here to tell a little bit about a project I had the opportunity to be part of.
00:45
It was a data journalism project and I've been using FOSS 4G tools to deal with the objectives of this project. And it's interesting to be in this session because we're going to have a little bit of different points of views.
01:08
We saw from Torsten and Ian and Andrea the point of view of the developer. With Ignó, we saw the point of view, the importance of building a community
01:23
to get more people interacting and using free and open source software for GIS. And now I will show a user case about pandemic situation and COVID-19 as well, but more in this project.
01:46
So before I start, just a little bit about me. I'm Felipe, Felipe Sódre Barros. I am a Brazilian geographer. Nowadays, I live in Argentina and I've been working with GIS and a lot of spatial analysis,
02:07
which nowadays calling as spatial data sciences or geographical data science. And I'm here to present about this project that we use it free and
02:26
open source software to face negationism and to face this pandemic situation of COVID-19. I'm pretty sure that you know this map already, right? This is the
02:42
Jon Snow cholera map related to the cholera outbreak on Soho neighborhood in London. And this is like a mandatory map or a presentation to be done in any GIS introduction course.
03:04
But most of them, I'm afraid to say, at least in Brazil, they stay only on this map showing that Jon Snow could relate the case of cholera death
03:21
with the spatial distribution, with the water plum, and then to help to overcome this situation. But the fact is, it's much more beyond that. In that situation, that case, in that specifically part of the history,
03:42
there was a paradigm called the miasma paradigm, which is the belief that all the disease was spread through the air. And this is really related to the historical situation, the time they were living, the tools they had, the industrial development.
04:14
And Jon Snow not only viewed this map, but together with other collaborators, he's
04:24
still working on it to organize the arguments to show to politicians or to managers to say that this is not a disease spread through the air. This is a new case.
04:44
This is something new, that there is a disease that can be spread through the water. So it's much more bigger than just the map itself. I usually suggest reading this book. It's the same book, but in Spanish, in Portuguese, and in English.
05:09
And it's, in my opinion, it's a good plot of the situation and the real importance of Jon Snow.
05:20
And by this time, you must be wondering, no, what, which negation is, what are you talking about? And it's a shame for myself to say that in 2018, if I'm not wrong, Brazilian majority population decide to have as a president Jair Bolsonaro.
05:46
And I bring here to show that according to the news, different kind of news, there is a situation caused by the lack of activity of Jair Bolsonaro
06:09
or worse, the way he is acting with this COVID-19 situation is getting worse, the situation.
06:22
And then this also, it's been seen as an attack to human rights because he's negating that there is a real, he's still negating that there is a real problem. He went to UN forum a few weeks ago.
06:43
He's not, he didn't use the vaccine. And this is a really dangerous situation. But this is not only a human health situation or a public health situation, but this is an environmental problem as well,
07:05
because they are using the fact that everybody are concerned with COVID-19 situation to flex the lies, the environmental laws and to, I have no word to say,
07:24
but it's like they take advantage of this calamity to flexibilize environmental law and not only in the environmental subject,
07:40
but also in the news, he's still posing a threat to press of freedom and also in the democracy. I've read this book, How Democracy Dies. It's an interesting book that tried to relate the Trump elections with and the way he managed the political situation with this,
08:15
how important it is in democracy to have subjects like the freedom of press not being attacked.
08:26
So in other words, free of press is something really, really important. It's a key thing on democracy beyond a lot of other subjects.
08:41
So I was invited in the beginning of this year to work together with Info Amazonia. It is a small and independent press who works basically with Amazon subjects, environmental, social, indigenous problems.
09:05
And they came up with this question, like, let's start with our work with a project trying to relate the situation,
09:23
the COVID-19 situation with the forest fires we have in Amazon. I will talk better about the project, but the name of the project is Inhaling Smoke, Beyond the Climate Change. It was supported by John Knight and Big Local News from Standard University,
09:47
which made it possible to develop this data journalism project. So about the objectives, we had to identify the best datasets about particulate matter smaller than 2.5 micrometers.
10:10
If in case you are not used with, the combustion smoke usually has this particulate matter smaller than 2.5.
10:24
Also the particulate matter smaller than 10 micrometer. And this one specifically is a really dangerous situation because it attacks the respiratory systems without COVID-19 situation.
10:50
It is already a really well known particulate that attacks the respiratory system. And so the idea was to identify the best datasets that we could identify the concentration especially
11:08
and validate these datasets with the temporal and spatial values with air pollution sensors. Then thanks to researchers from ACRI University, they are using, they are already working with these those kinds of analysis.
11:30
They have installed air pollution sensors in ground. So we had, like we used those pollution sensor as truth, ground truth to validate with the others dataset we had, we could use.
11:50
Then validating estimate or model the correlation, not only the correlation, but of the particulate matter with forest fire occurrence
12:03
and to identify and then this is the core object of this work to identify if there is a relation between the exposure to high PM particulate matter values with hospital admission and length of stay by respiratory syndromes caused by COVID-19.
12:28
So just a small disclaimer, we are not relating the particulate matter with the spread of the COVID-19. No, we are relating to if there is any increase on the hospital admission related to the exposure on the particulate matter.
13:00
So a little bit about the particulate matter I'm saying, this is a hair and so you can have a dimension of the size. This is a fine bit sand and we have particulate matter of smaller than 10 microns in diameter.
13:25
And we have the particulate matter smaller than 2.5 microns in diameter. And this one is the one we was working because it's the well-known as a problem for respiratory system.
13:43
So about the special data sets, we came up with the Copernicus atmosphere monetary service. They has a data set specifically forecasting the particulate matter.
14:02
And this forecast is a huge model with a lot of inputs from meteorological stations. And we came up with this one as the best one for us because of the special resolution and the time resolution as well.
14:24
And this data set, they run daily models for every three hours, predicting until five days ahead. But we decided to use for 2012.
14:42
Sorry, I forgot to say that this analysis was run for only the year 2012. And we got the models run in the day for the day for the every three hours for the same day to get the most restrict forecast.
15:04
In total, we could access 2920 images, which I processed then using R and slash Python. Actually, the most processed were done in R using the stars package, an amazing package that allows me to process everything really, really fast.
15:33
And the public health about the hospital admission, we use the data source, which is the public health system in Brazil.
15:47
Although all problems we had on the president trying to hide those information we could use or in the case we needed, we access the state level data on healthy information, health information.
16:08
So about the validation, we are seeing here the values from comes in red and yellow. The sensors from the Acre University, they have not a lot of sensors, but the ones they have, we use it to validate.
16:31
And we could see that during the fires season in Amazon is a well-known season. It was kind of natural fire season, but nowadays it's been like a long time.
16:48
It's not natural. It is also a human activity and not any kind of human. There are people who do those fire associated with deforestation, with mining and others activities.
17:06
So just to show you that we could realize that temporal and spatially, we could realize that the trend on sensors, on the ground truth are really shown in the predicted on the model we are using, the data set we are using.
17:31
And then we are showing here. We have like several municipalities. The line is in white. And then we have suggest or a limit from World Health Association.
17:53
I don't remember the name exactly, but the World Health Organization, they suggest that the daily concentration of exposure shouldn't be higher than 25 grams per meters cubic.
18:13
Okay, so this is the line here, the line here showing the limit. And then, of course, few municipalities before they burned the fire season,
18:27
they already was presenting daily exposure higher than they suggested by the Global Health Organization. And then during the fire season, not all municipalities, but a lot of those got really worse situation.
18:54
So attention to, I'm saying to daily exposure, daily exposure.
19:02
So all day exposed to a value higher than 25 grams per meters cubic. So the key finds we could achieve that for each day of exposure,
19:24
bigger than the suggested from the World Health Organization, increase in 2% the probability of a person being hospitalized.
19:41
So it's like to developing worse scenarios of COVID, of actually of respiratory problem. The person supposing that a person already with COVID, they will probably develop a problem on the respiratory system,
20:05
then being exposed for each day exposed on this high level will increase in 2% the probability of this person to be hospitalized on a worst case of respiratory situation.
20:21
Also that the smoke fires was related to an increase of 18% in severe cases of COVID and 24% in admission for respiratory syndromes. I mean, we could split the persons with COVID-19
20:44
and then people that had respiratory problem that wasn't diagnosed with COVID-19. So it's like a quarter of 24% in admission for five states in Amazon.
21:06
And here I'm showing, I won't tell about this info graph, but the states are Matugroso, Rondonia, which was the worst one, the worst place to be, and Acri, Amazonas, and Para.
21:25
Those five states that we could associate to increase of 18% of severe cases of COVID and 19% of case of respiratory syndromes.
21:45
Yeah, but this is true talking in a large case, but if we go to municipality cases, we can see like for instance, Paoini in Amazon state had all the 30 days of August
22:06
with higher values than 25 grams per meter cubic. All the days with higher level. And this is represented as increasing on the 18% of the hospital admissions for COVID
22:42
and 115 for respiratory syndrome. Others as well had pretty much the same situation. And then I came up with the part of the reports.
23:02
This is a work of data journalists. So it's not only get on this model case or scientific case, but also to understand what's going on to the people. So I bring here the case of Tania Silva.
23:21
She is on the report we have published. She was pregnant. She got COVID in the first season. She got really bad. She was pregnant and had to have her baby before so both could be alive. She is quite fine nowadays.
23:42
She is still living with the sequels of the case she had. And perhaps you are now asking, okay, Felipe, but why are you talking about Tania and not about other person? A lot of people includes diet about it.
24:03
Yeah, this is the point. The point is here to use all this scientific approach to understand the worst place, the place that was most affected, to go there to talk to people and to understand the situation of these people
24:23
that are most exposed because of different situation, pretty much what Jon Snow did on the cholera outbreak. Tania lives on a quilombo.
24:43
I should mention here that in Spanish, quilombo is used to a messy situation. In Portuguese, it's like a way to name a community of original people from or related with the slavery process we had in Brazil.
25:05
Okay, so she is living in a small community far from the hospital and then the idea is really to show that there are people suffering a lot being a part of the health system that then should be the case.
25:27
Also, Raimundo, he lives, works with the forest, all the things he gets, he strikes from the forest.
25:43
He fought together with Xico Mendes, which is a person, a known person in Brazil that fought the deforestation process, showing people that they can live with the forest, getting their incomes from the forest.
26:02
He fought and now he is in this situation. He got contracted COVID, got really bad situation. He is alive, but he lives in a structivism community, which is the one identified as the most attacked
26:21
by the deforestation and the people that are trying to put another, use the land on other's way. Also, the last one, Bepto Quirin. I don't know how to say. He is a leader, an indigenous leader.
26:41
We say Cacique in Portuguese. He passed away, unfortunately, and he is in an indigenous land, which is unfortunately the land most attacked for people trying to mine in their land
27:01
and to using fire for deforestation. And so it's not good news I'm bringing here, but it's like trying to show people what is the idea on the data journalism. This is the team I worked with.
27:23
Juliana Mori is the leader of the project. She works for Informazonia, worked together on this scientific part and then all the reporters. And we will leave a few related links I will share
27:42
or you can reach me on VenuList. I can share my presentation. The idea is you have every report I showed here you can read and also the reports we have done. So pretty much is that.
28:03
I'm glad to, I think I'm on time. And just to say that thanks, special thanks to all developers, to all the people organizing community. We are using, we are trying our best
28:21
to use the software you are developing to get better lives, not only for ourselves, but to the others and then to face this situation of negationism and this bad situation of pandemic.
28:45
Thanks and I'm available for any question. Thank you. We are just at the end of the session, but since there is a break afterwards,
29:00
we can stay a couple of minutes for having the questions. I'm going to read through the questions if you like right now. Okay, so what action did this work call for and any positive responses from responsible organizations so far? So far, unfortunately,
29:21
and we wasn't expecting any activity from the government because they are negating, they are going against the situation. So the idea is like share those histories
29:41
to show that a lot of people suffering. I'm not in the informal zone, but I'm sure we associated with others big press to spread this situation. So I'm not sure about how is the activity,
30:03
if there is something changing, but in Brazil, I don't think so, but the idea is to articulate and I know that there are a lot of investigations going on internationally includes to see the situation we are facing.
30:28
Okay, thank you. And the next question is from the audience. Based on the results of this work, what do you expect in a future scenario of intensifying the effects of climate change?
30:41
So what are you expecting in the future? Tough question. Unfortunately, with all the results we got, I'm not able to expect something better than we are facing. We run this analysis.
31:01
As you can see, I didn't put much effort on showing here about the model we use it, but it would be interesting to go further on trying to understand how would be the situation on climate change,
31:21
but I don't think we'll be better than we are seeing nowadays. Okay, thank you very much. There are no questions, but there are lots of comments congratulating
31:41
and thanking you for your great presentation now. So I'm going to thank you as well for this great presentation on this topic and also thank you for your efforts within the organizing committee. So we are hoping that we will have a great event throughout the week.
32:03
Yes, thank you everybody. I'm available to talk about and have a good conference. Okay, thank you very much. We are going to have a break at the conference right now, so I think it is in one hour or two hours.
32:23
Just let me check the schedule. Sorry about that. Yes, the sessions will continue in one hour. There will be a talk and then the sessions will continue. Thanks a lot and thanks for joining. Thanks for watching this session. Hope to meet you.